Methods and apparatus for inferring user intent based on neuromuscular signals

ABSTRACT

Methods and system for predicting the onset of a motor action using neuromuscular signals. The system comprises a plurality of sensors configured to continuously record a plurality of neuromuscular signals from a user and at least one computer processor programmed to provide as input to a trained statistical model, the plurality of neuromuscular signals or information based on the plurality of neuromuscular signals, predict, based on an output of the trained statistical model, whether an onset of a motor action will occur within a threshold amount of time; and send a control signal to at least one device based, at least in part, on the output probability, wherein the control signal is sent to the at least one device prior to completion of the motor action by the user.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit under 35 U.S.C. § 119(e) of U.S.Provisional Patent Application Ser. No. 62/366,419, filed Jul. 25, 2016,and entitled “Method and apparatus for inferring user intention,” theentire contents of which is incorporated by reference herein.

BACKGROUND

Neuromuscular signals arising from the human central nervous systemprovide information about neural activation that results in thecontraction of one or more muscles in the human body. The neuromuscularsignals may measure the neural activation, the muscle excitation, themuscle contraction, or a combination of the neural activation and themuscle contraction. Electromyography (EMG) sensors placed on the surfaceof the human body record electrical activity produced by skeletal musclecells upon their activation. Signals recorded by EMG sensors areroutinely used to assess neuromuscular dysfunction in patients withmotor control disorders and have been used in some applications ascontrol signals for devices such as prosthetic limbs.

SUMMARY

Biophysical sensors such as EMG sensors record biological signals inadvance of the onset of motor activity. In the case of EMG sensorsarranged on the surface of the human body, the biological signalsrecorded relate to the generation of action potentials in muscle fibersof muscles of the human body. Some embodiments are directed to analyzingEMG signals using a trained statistical model to predict the onset of amotor task prior to performance of the motor task. Control signalsdetermined based on the model predictions may be used to control theoperation of devices with short latency.

Controlling devices based, at least in part, on a reliable prediction ofan onset of motor task rather than waiting until a user has completedthe motor task provides for an improvement to conventional techniquesfor controlling devices. Non-limiting illustrative applications includereplacements or enhancements for buttons or triggers/switches for gamesthat require fast reaction times, steering or other operating inputs forphysical or virtual vehicles including cars, enhanced control ofjoysticks or navigational controls, and manipulation of objects within avirtual reality environment, such as picking up or throwing virtualballs.

Some embodiments are directed to a control system. The control systemcomprises a plurality of sensors configured to continuously record aplurality of neuromuscular signals from a user, and at least onecomputer processor programmed to provide as input to a trainedstatistical model, the plurality of neuromuscular signals and/orinformation based on the plurality of neuromuscular signals, predict,based on an output of the trained statistical model, whether an onset ofa motor action will occur within a threshold amount of time, and send acontrol signal to at least one device based, at least in part, on theoutput probability, wherein the control signal is sent to the at leastone device prior to completion of the motor action by the user.

Other embodiments are directed to at least one non-transitory computerreadable medium encoded with a plurality of instructions that, whenexecuted by at least one computer processor perform a method. The methodcomprises providing, as input to a trained statistical model, aplurality of neuromuscular signals recorded from a plurality of sensorsarranged on or around a part of a user's body, predicting whether anonset of a motor action will occur within a threshold amount of time,and sending a control signal to at least one device based, at least inpart, on the output probability, wherein the control signal is sent tothe at least one device prior to completion of the motor action by theuser.

Other embodiments are directed to a method of predicting an onset of amotor action. The method comprises providing, as input to a trainedstatistical model, a plurality of neuromuscular signals recorded from aplurality of sensors arranged on or around a part of a user's body,predicting, using at least one computer processor, whether an onset of amotor action will occur within a threshold amount of time, and sending acontrol signal to at least one device based, at least in part, on theoutput probability, wherein the control signal is sent to the at leastone device prior to completion of the motor action by the user.

Other embodiments are directed to a computer system for training astatistical model to predict an onset of a motor task based, at least inpart, on neuromuscular signal data. The computer system comprises aninput interface configured to receive the neuromuscular signal datarecorded during performance of a motor action performed by one or moreusers, receive result data indicating an outcome of the motor actionperformed by the one or more users. The computer system furthercomprises at least one storage device configured to store a plurality ofinstructions that, when executed by at least one computer processorperform a method of generating training data based, at least on part, onthe received neuromuscular signal data and the received result data,training the statistical model using at least some of the generatedtraining data to output a trained statistical model, determining anoperating threshold used to interpret output of the trained statisticalmodel, and storing, by the at least one storage device, the trainedstatistical model and the operating threshold, wherein the trainedstatistical model is configured to predict the onset of a motor taskprior to completion of the motor task by a user.

It should be appreciated that all combinations of the foregoing conceptsand additional concepts discussed in greater detail below (provided suchconcepts are not mutually inconsistent) are contemplated as being partof the inventive subject matter disclosed herein. In particular, allcombinations of claimed subject matter appearing at the end of thisdisclosure are contemplated as being part of the inventive subjectmatter disclosed herein.

BRIEF DESCRIPTION OF DRAWINGS

Various non-limiting embodiments of the technology will be describedwith reference to the following figures. It should be appreciated thatthe figures are not necessarily drawn to scale.

FIG. 1 is a flowchart of a biological process for performing a motortask in accordance with some embodiments of the technology describedherein;

FIG. 2 is a schematic diagram of a computer-based system for predictingan onset of one or more motor tasks in accordance with some embodimentsof the technology described herein;

FIG. 3 is a flowchart of an illustrative process for generating astatistical model for predicting the onset of one or more motor tasksusing neuromuscular signals, in accordance with some embodiments of thetechnology described herein;

FIG. 4 is a flowchart of an illustrative process for using a trainedstatistical model to predict the onset of one or more motor tasks usingneuromuscular signals, in accordance with some embodiments of thetechnology described herein.

FIG. 5A illustrates a wristband having EMG sensors arrangedcircumferentially thereon, in accordance with some embodiments of thetechnology described herein; and

FIG. 5B illustrates a user wearing the wristband of FIG. 5A while typingon a keyboard, in accordance with some embodiments of the technologydescribed herein.

DETAILED DESCRIPTION

Coordinated movements of skeletal muscles in the human body thatcollectively result in the performance of a motor task originate withneural signals arising in the central nervous system. The neural signalstravel from the central nervous system to muscles via spinal motorneurons, each of which has a body in the spinal cord and axon terminalson one or more muscle fibers. In response to receiving the neuralsignals, the muscle fibers contract resulting in muscle movement.

FIG. 1 illustrates a flowchart of a biological process 100 forinitiating a motor task by the coordinated movement of one or moremuscles. In act 102, action potentials are generated in one or moreefferent spinal motor neurons. The motor neurons carry the neuronalsignal away from the central nervous system and toward skeletal musclesin the periphery. For each motor neuron in which an action potential isgenerated, the action potential travels along the axon of motor neuronfrom its body in the spinal cord where the action potential is generatedto the axon terminals of the motor neuron that innervate muscle fibersincluded in skeletal muscles.

A chemical synapse formed at the interface between an axon terminal of aspinal motor neuron and a muscle fiber is called a neuromuscularjunction. As an action potential transmitted along the axon of a motorneuron reaches the neuromuscular junction, process 100 proceeds to act104, where an action potential is generated in the muscle fiber as aresult of chemical activity at the neuromuscular junction. Inparticular, Acetylcholine released by the motor neuron diffuses acrossthe neuromuscular junction and binds with receptors on the surface ofthe muscle fiber triggering a depolarization of the muscle fiber.Although neuromuscular signals sensed on the body surface generated byindividual muscle fibers are small (e.g., less than 100 μV), thecollective action of multiple muscle fibers conducting simultaneouslyresults in a detectable voltage potential that may be recorded byneuromuscular (e.g., EMG) sensors located on the surface of the body.

Following generation of an action potential in the muscle fiber, process100 proceeds to act 106, where the propagation of the action potentialin the muscle fiber results in a series of chemical-mediated processeswithin the muscle fiber. For example, depolarization of a muscle fiberresults in an influx of calcium ions into the muscle fiber. Calcium ionsinside the muscle fiber bind with troponin complexes causing thetroponin complexes to separate from myosin binding sites on actinfilaments in the muscle fiber, thereby exposing the myosin bindingsites.

Following these chemical-mediated processes, process 100 proceeds to act108, where the muscle fiber contracts. Muscle fiber contraction isachieved due to the binding of exposed myosin heads with actin filamentsin the muscle fiber creating cross-bridge structures. Process 100 thenproceeds to act 110, where the collective contraction of muscle fibersin one or more muscles results in the performance of a motor task. Themotor task may be a simple task such as a button press, which involvesonly a few muscles in a finger and/or wrist, a more complex task such asgrasping and turning a doorknob involving several muscles of the hand,wrist and arm, or a motor task of any other complexity, as embodimentsof the technology described herein are not limited in this respect.

Neural activity, muscle fiber recruitment, muscle contraction and jointmovement all precede the completion of a motor task. For example, thechemical-mediated and mechanical processes involved in acts 106 and 108of process 100 are not instantaneous, but occur over a time period,which may be on the order of hundreds of milliseconds. Accordingly,there is a time delay between when neuromuscular sensors placed on ornear the body surface record the generation of action potentials in themuscle fibers at act 104 in process 100 and when the motor task isperformed in act 110. The inventors have recognized and appreciated thatrather than waiting until the intentional action is performed, signalsrecorded from neuromuscular sensors may be used to predict the motortask to be performed in advance of the task actually being performed bythe wearer of the sensors.

Throughout this disclosure electromyography (EMG) sensors are used asexamples of the type of neuromuscular sensors configured to detectneuromuscular activity. However it should be appreciated that othertypes of neuromuscular sensors including, but not limited to,mechanomyography (MMG) sensors and sonomyography (SMG) sensors mayadditionally or alternatively be used in combination with EMG sensors todetect neuromuscular activity in accordance with some embodiments. Theneuromuscular signals recorded by the neuromuscular sensors may be usedto predict the onset of one or more motor tasks being performed by thewearer of such sensors.

Accordingly, some embodiments are directed to predicting with shortlatency (e.g., prior to a motor task being performed), based on recordedneuromuscular signals (e.g., EMG signals, MMG signals, and SMG signals),the onset of the motor task using a statistical model trained to modelneuromuscular activity leading to the performance of the motor task. Insome embodiments, the neuromuscular signals are recorded continuouslyand predictions are made based on the continuously recordedneuromuscular signals. As a non-limiting example of a simple motor taskof pressing a button, some embodiments determine, based on a pluralityof neuromuscular signals, a likelihood that the button will be pressedprior to the user actually pressing the button. In some embodiments theprediction can be made 10 milliseconds prior to the action beingperformed, in other embodiments the prediction can be made 50milliseconds, 100 milliseconds, 200 milliseconds, or 250 millisecondsprior to the task being performed. The prediction may be made 50-100milliseconds, 100-200 milliseconds, or 200-300 milliseconds prior to thetask being performed in some embodiments. The prediction of a user'sintention to perform a motor task in accordance with some embodimentscan be used to control devices at short latency, as discussed in moredetail below.

FIG. 2 illustrates a system 200 in accordance with some embodiments. Thesystem includes a plurality of neuromuscular sensors 210 configured torecord signals arising from neuromuscular activity in skeletal muscle ofa human body. Neuromuscular sensors 210 may include one or more EMGsensors, one or more MMG sensors, one or more SMG sensors, and/or one ormore sensors of any suitable type that are configured to detectneuromuscular signals. Typical EMG sensors include electrodes whichdetect electric potentials on the surface of the body and hardwareprocessing circuitry that processes the raw EMG signal to performamplification, filtering (e.g., low pass, high pass, band pass, shaping,narrow band, wide band, temporal etc.), or other types of signalprocessing (e.g., rectification). Some embodiments employ EMG sensorsincluding hardware signal processing circuitry for processing recordedEMG signals. Other embodiments employ EMG sensors, where at least someof the processing circuitry is performed by one or more circuits incommunication with, but not directly integrated with the electrodes thatrecord the signals. In other embodiments, at least some (e.g., all) ofthe signal processing (e.g., amplification, filtering, rectification,etc.) may be implemented using software rather than by using hardwaresignal processing circuitry. Thus, signal processing of EMG signals(e.g., amplification, filtering, and rectification) may be performed inhardware only, in software only, or by any combination of hardware andsoftware, as aspects of the technology described herein are not limitedin this respect.

In some embodiments, neuromuscular sensors 210 include one or more MMGsensors and/or one or more SMG sensors in addition to or instead of EMGsensors. When used, MMG and SMG sensors may be of any suitable type, asaspects of the technology described herein are not limited in thisrespect. Some embodiments employ MMG and/or SMG sensors that includehardware signal processing circuitry for performing signal processing(e.g., amplification, filtering, and rectification) on recorded MMGand/or SMG signals. In other embodiments, at least some signalprocessing of the MMG and/or SMG signals may be performed in software.Thus, signal processing of MMG and/or SMG signals may be performed inhardware only, in software only, or by any suitable combination ofhardware and software, as aspects of the technology described herein arenot limited in this respect.

In some embodiments, the plurality of neuromuscular sensors 210 includesone or more pairs of neuromuscular sensors arranged as a portion of awearable device configured to be worn on or around part of a user'sbody. For example, in one non-limiting example, a plurality ofneuromuscular sensors may be arranged circumferentially around anadjustable and/or elastic band such as a wristband or armband configuredto be worn around a user's wrist or arm.

In one implementation, 16 EMG sensors are arranged circumferentiallyaround an elastic band configured to be worn around a user's lower arm.For example, FIG. 5A shows EMG sensors 504 arranged circumferentiallyaround elastic band 502. It should be appreciated that any suitablenumber of EMG sensors may be used and the particular number andarrangement of EMG sensors used may depend on the particular applicationfor which the wearable device is used. For example, a wearable armbandor wristband may be used to predict hand-based motor tasks such aspressing button or moving a joystick, whereas a wearable leg or ankleband may be used to predict foot-based motor tasks such as pressing thegas or brake pedal on a vehicle such as a real or virtual car. Forexample, as shown in FIG. 5B, a user 506 may be wearing elastic band 502on hand 508. In this way, EMG sensors 504 may be configured to recordEMG signals as a user controls keyboard 512 using fingers 510.

In some embodiments, multiple wearable devices, each having one or moreEMG sensors included thereon may be used to predict the onset of complexmotor tasks that involve multiple parts of the body.

System 200 also includes one or more computer processors 212 programmedto communicate with sensors 210. For example, signals recorded bysensors 210 may be provided to processor(s) 212 for processing.Processor(s) 212 may be implemented in hardware, firmware, software, orany combination thereof. Additionally, processor(s) 212 may beco-located on the same wearable device as the neuromuscular sensors 210or may be at least partially located remotely (e.g., processing mayoccur on one or more network-connected processors).

System 200 also includes datastore 214 in communication withprocessor(s) 212. Datastore 214 may include one or more storage devicesconfigured to store information describing a statistical model used forprediction of the onset of motor tasks in accordance with someembodiments. Processor(s) 212 may be configured to execute one or moremachine learning algorithms that process signals output by the sensor(s)210 to train a statistical model stored in datastore 214, and thetrained (or retrained) statistical model may be stored in datastore 214for later use in predicting the onset of a motor task. Non-limitingexamples of statistical models that may be used in accordance with someembodiments to predict the onset of a motor task based on recordedneuromuscular signals are discussed in more detail below.

In some embodiments, processor(s) 212 may be configured to communicatewith neuromuscular sensors 210, for example to calibrate the sensorsprior to measurement of neuromuscular signals. For example, a wearabledevice may be positioned in different orientations on or around a partof a user's body and calibration may be performed to determine theorientation of the wearable device and/or to perform any other suitablecalibration tasks. Calibration of neuromuscular sensors 210 may beperformed in any suitable way, and embodiments are not limited in thisrespect. For example, in some embodiments, a user may be instructed toperform a particular sequence of movements and the recordedneuromuscular activity may be matched to a template by virtuallyrotating and/or scaling the signals detected by the sensors (e.g., bythe electrodes on EMG sensors). In some embodiments, calibration mayinvolve changing the gain(s) of one or more analog to digital converters(ADCs), for example, in the case that the signals detected by thesensors result in saturation of the ADCs.

System 200 also includes one or more devices 216 configured to becontrolled based, at least in part, on processing by processor(s) 212.As discussed in more detail below, processor(s) 212 may implement atrained statistical model 214 configured to predict the onset of a motortask based, at least in part, on neuromuscular signals recorded bysensors 210 (e.g., EMG sensors, MMG sensors, and SMG sensors), and oneor more control signals determined based on the predicted onset of themotor task may be sent to device 216 to control one or more operationsof the device with a latency shorter than would be achieved if thecontrol signal was not sent until motor task completion. In someembodiments, device 216 may be controlled with a latency of a durationthat is not perceptible, difficult to perceive, or unlikely to beperceived by humans, or with a latency of a duration that isimperceptible to a person with ordinary sensory perception.

Device 216 may include any device configured to receive control signalsthrough a control interface. Non-limiting examples of devices includeconsumer electronics devices (e.g., television, smartphone, computer,laptop, telephone, video camera, photo camera, video game system,appliance, etc.), vehicles (e.g., car, marine vessel, manned aircraft,unmanned aircraft, farm machinery, etc.), robots, weapons, or any otherdevice that may receive control signals through one or more controlinterfaces.

A device 216 may be controlled through any suitable type of controlinterface. A control interface may be implemented using hardware,software, or any suitable combination thereof. For example, a device 216may be a video game system which may be controlled through a gamecontroller. As another example, a device 216 may be a computing device,which may be controlled through a keyboard, keypad, and/or a mouse. Asanother example, a device may be a computing device, which may be touchcontrolled through a graphical user interface generated by atouch-screen display. As another example, a device may be a vehicle(e.g., a car, an aircraft, a marine vessel, an unmanned aerial vehicle,etc.), which may be controlled through one or more mechanical controldevices (e.g., pedals, wheel, joystick, paddles, levers, knobs, etc.).

In some embodiments, system 200 may be trained to predict the onset ofone or more motor actions performed by the user. The motor actions mayinclude control actions a user takes with respect to a control interfaceof a device of devices 216. For example, when the control interface of adevice includes one or more buttons, the system 200 may be trained topredict whether a user will press one or more of the buttons within athreshold amount of time. In some embodiments, the system 200 may betrained by recording the neuromuscular signals of one or more users asthe user(s) provide input through a control interface of a device. Aftersuch training, the system 200 may be configured to predict, based on aparticular user's neuromuscular signals, whether the user will performone or more control actions with respect to the control interface.

In some embodiments, after system 200 is trained to predict, based on aparticular user's neuromuscular signals, whether the user will performone or more control actions with respect to the control interface of adevice, a user may utilize the system 200 to control the device withoutthe control interface. For example, when the system 200 is trained topredict the control actions that the user intends to take with highaccuracy (e.g., at least a threshold accuracy), the predictionsthemselves may be used to control the device.

In some embodiments, a user may utilize a combination of the system 200and the control interface to control a device. For example, when thesystem 200 generates a prediction of the control action that the userwill take with respect to the control interface and the prediction isgenerated with at least a threshold amount of confidence and/or within athreshold amount of time of when the predicted action is to take place,the prediction may be used to generate a control signal and the system200 may control the device. On the other hand, if the prediction isgenerated with lower than a threshold confidence or is generated too farin advance, the system 200 may be configured to not use such aprediction to control the device. In that case, the user may control thedevice directly through the control interface.

Some embodiments include a feedback system configured to be initiatedbased on a control signal provided processor(s) 212 in accordance withsome embodiments. The feedback system may represent a final motor taskto be performed and may be initiated simultaneously with taskcompletion. In some embodiments, the feedback system may be configuredto provide feedback using haptic technology or using a buzzer system.

As discussed above, some embodiments are directed to using a statisticalmodel for predicting the onset of a motor task prior to completion ofthe motor task by a user. The statistical model may be used to predictthe onset of a motor task based on neuromuscular signals (e.g., EMG,MMG, and SMG signals) detected as a result of neuromuscular activitythat occurs in advance of the motor task onset. FIG. 3 describes aprocess 300 for generating (sometimes termed “training” herein) such astatistical model from EMG signals recorded for one or more users priorto the user(s) performing one or more motor task(s). Although process300 is described herein with respect to EMG signals, it should beappreciated that process 300 may be used to train a statistical modelfor predicting the onset of a motor task based on EMG signals, MMGsignals, SMG signals, or any suitable combination thereof.

Process 300 may be executed by any suitable computing device(s), asaspects of the technology described herein are not limited in thisrespect. For example, process 300 may be executed by processors 212described with reference to FIG. 2. As another example, one or more actsof process 300 may be executed using one or more servers (e.g., serverspart of a cloud computing environment). For example, at least a portionof act 310 relating to training of a statistical model (e.g., a neuralnetwork) may be performed using a cloud computing environment.

Process 300 begins at act 302, where a plurality of neuromuscularsignals is obtained for one or multiple users performing one or moremotor tasks. In some embodiments, the plurality of EMG signals may berecorded as part of process 300. In other embodiments, the plurality ofEMG signals may have been recorded prior to the performance of process300 and are accessed (rather than recorded) at act 302.

In some embodiments, the EMG signals obtained at act 302 may bepre-processed using amplification, filtering, rectification or othertypes of signal processing. In some embodiments, the filtering maycomprise temporal filtering implemented using convolution operationsand/or equivalent operations in the frequency domain (e.g., after theapplication of a discrete Fourier transform).

In some embodiments, the plurality of EMG signals may include EMGsignals recorded for a single user performing one or multiple motortasks. The user may be instructed to perform a motor task (e.g.,pressing one of two buttons) and EMG signals corresponding to the user'sneuromuscular activity may be recorded as the user performs the motortask he/she was instructed to perform. The EMG signals may be recordedby any suitable number of EMG sensors located in any suitablelocation(s) to detect the user's neuromuscular activity that is relevantto the motor task. For example, after a user is instructed to perform amotor task with the fingers of his/her right hand, the EMG signals maybe recorded by multiple EMG sensors circumferentially (or otherwise)arranged around the user's lower right arm. As another example, after auser is instructed to perform a motor task with his/her leg (e.g., topush one of two pedals, for example, either a gas or brake pedal in acar), the EMG signals may be recorded by multiple EMG sensorscircumferentially (or otherwise) arranged around the user's leg.

In some embodiments, the EMG signals may be recorded at multiple timepoints as a user performs a motor task. As a result, the recorded EMGsignals may include EMG data obtained by multiple EMG sensors at each ofmultiple time points. Assuming that n EMG sensors are arranged tosimultaneously measure the user's neuromuscular activity duringperformance of the motor task, the recorded EMG signals for the user maycomprise a time series of K n-dimensional vectors {x_(k)|1≦k≦K} at timepoints t₁, t₂, . . . , t_(K) prior and up to the completion of the motortask.

In some embodiments, a user may be instructed to perform a motor taskmultiple times and the user's neuromuscular activity may be recorded foreach of multiple repetitions of the task by the user. In someembodiments, the plurality of EMG signals may include EMG signalsrecorded for multiple users, each of the multiple users performing thesame motor task one or more times. Each of the multiple users may beinstructed to perform the motor task and EMG signals corresponding tothat user's neuromuscular activity may be recorded as the user performs(once or repeatedly) the motor task he/she was instructed to perform.When EMG signals are collected by multiple users which are combined togenerate a statistical model, an assumption is that different usersinvoke similar neuromuscular activity for performing the same motortask. Collecting EMG signals from a single user performing the same taskrepeatedly and/or from multiple users performing the same motor task oneor multiple times facilitates the collection of sufficient training datato generate a statistical model that can accurately predict the onset ofa motor task prior to the performance of the motor task. In someembodiments, a user-independent statistical model may be generated basedon training data corresponding to the recorded EMG signals from multipleusers, and as the system is used by a user, the statistical model isretrained based on recorded EMG data such that the statistical modellearns the user-dependent characteristics to refine the predictioncapabilities of the system for the particular user.

In some embodiments, the plurality of EMG signals may include EMGsignals recorded for a user (or each of multiple users) performing eachof multiple motor tasks one or multiple times. For example, a user maybe instructed to perform each of multiple tasks (e.g., pressing thefirst of two buttons on a keypad and pressing the second of two buttonson the keypad) and EMG signals corresponding to the user's neuromuscularactivity may be recorded as the user performs each of the multiple motortasks he/she was instructed to perform. Collecting such EMG data mayfacilitate developing a statistical model for predicting the onset ofone of multiple different actions that may be taken by the user. Forexample, training data that incorporates responses to multiple actionsmay facilitate generating a statistical model for predicting which ofmultiple possible inputs a user may provide to one or more controldevices (e.g., a keypad, a joystick, a mechanical control input, aninput to a graphical user interface, etc.) in the user's environment.

As discussed above, the EMG data obtained at act 302 may be obtained byrecording EMG signals as each of one or multiple users performs each ofone or more actions one or more multiple times. The results of the tasksperformed by these user(s) may be obtained at act 304 (e.g., whichbutton was pressed, which input was provided, which controller wasmoved). As one non-limiting example, EMG data obtained at act 302 mayinclude EMG signals as a user is performing the task of pressing aparticular button on a keypad. In this example, information indicatingwhich button was pressed by the user (e.g., the button “A”) is obtained.In this way, the actions performed by the user(s) are recorded inaddition to recording the EMG signals preceding the performance of thetasks. In some embodiments, when EMG signals are recorded prior to theexecution of process 300 and are accessed at act 302, the results of thetasks performed by the users may also be recorded prior to the executionof process 300 and may be accessed at act 304. In other embodiments, theresults of the tasks may be recorded at act 304.

Next, process 300 proceeds to act 306, where a prediction timescalethreshold may optionally be obtained. The prediction timescale thresholdprovides an indication of how far into the future the statistical modelbeing generated as part of process 300 will attempt to predict the onsetof a user action. For example, if the prediction timescale threshold isset at 250 milliseconds (ms), then the trained statistical modelgenerated as part of process 300, may be used to predict, based on EMGsignals obtained from a user, the probability that the onset of a motortask will occur in the next 250 milliseconds. It should be appreciatedthat this prediction timescale threshold is different from the operatingthreshold described below with reference to act 312. The operatingthreshold relates to a probability threshold rather than being atime-based threshold.

Any suitable prediction timescale threshold (including no predictiontimescale threshold) may be used, as aspects of the technology describedherein are not limited in this respect. Setting this threshold too high(e.g., multiple seconds) may result in additional computation beingperformed, but is unlikely in practice to provide any added performancebenefit because the neuromuscular activity being processed by the modelmay not correlate with the performance of the motor task performed sofar in the future. For example, during that time there may have beenintervening events resulting in the user changing their mind about whattask to perform. Setting the prediction timescale threshold too low(e.g., on the order of microseconds) may result in reliable predictions,but such predictions may have limited value as they are close to thetime when the onset of the task is performed by the user. Accordingly,in some embodiments, the prediction timescale threshold is not set ateither of these extremes. In some embodiments, the prediction timescalethreshold may be at least 50 ms, at least 100 ms, at least 250 ms, atleast 500 ms, at least 1 s, between 50 and 250 ms, between 100 and 500ms, between 200 ms and 800 ms, or in any other suitable range in theseranges. In embodiments in which a prediction timescale threshold is notset (or when the prediction timescale threshold is set at a sufficientlylong value), the statistical model may nonetheless be trained usingrecorded EMG signals, though the learning process for the model may takelonger than if a suitable prediction threshold for the motor task orseries of motor tasks was set.

Next, process 300 proceeds to act 308, where the data obtained at acts302 and 304 is combined to create training data used for training astatistical model at act 310. The obtained data may be combined in anysuitable way. In some embodiments, each of the EMG signals obtained atact 302 may be associated with an action corresponding to the result ofthe motor task being performed by the user when the EMG signals wererecorded. For example, EMG signals obtained at time greater than theprediction timescale threshold in advance of the user performing a motortask may be associated with a “no action” label and EMG signals obtainedbetween a time corresponding to the prediction timescale thresholdamount before the onset of the motor task and the onset of the motortask may be labeled with the task that was performed by the user. Inthis way, the statistical model may be trained to predict the occurrenceof an action within the prediction timescale threshold of time into thefuture.

Act 308 is further illustrated in Table 1, which shows that EMG signalsobtained at a time greater than the prediction timescale threshold inadvance of a user pressing the button “A” are labeled with the name ofthe action (i.e., “A”), whereas EMG signals obtained earlier than thepredication timescale threshold are labeled with the label “no action.”In this example, the prediction timescale threshold is at a time betweenwhen the EMG signals x₃ and x₄ were recorded.

TABLE 1 No Action No Action No Action A A A A x₁ x₂ x₃ x₄ x₅ x₆ x₇Training data consisting of EMG signals x₁ . . . x₇ recorded by EMGsensors worn by a user after the user is instructed to push the button“A” and before the user pushed this button. The EMG signals areassociated with corresponding actions including “A” and “no-action.”

As a non-limiting example, the EMG signals obtained at act 302 for aparticular user may comprise a sequence of vectors {x_(k)|1≦k≦K}generated using measurements obtained at time points t₁, t₂, . . . ,t_(K), where the ith component of vector x_(j) is a value measured bythe ith EMG sensor at time t_(j) and/or derived from the value measuredby the ith EMG sensor at time t_(j). Let M denote the predictiontimescale threshold. If the user performed the task (e.g., pressed thebutton “A” that the user was instructed to press) at time T, then anyvector x_(k) obtained at a time t_(k) that occurs within M of T (i.e.,|T−t_(k)|≦M) may be associated with the result of the task (e.g., withinformation indicating that the user pressed the button “A”). On theother hand, any vector x_(k) obtained at a time t_(k) that does notoccur within M of T (i.e., |T−t_(k)|>M) may be associated with the “noaction” result (e.g., the user did not press the button “A” within theprediction timescale threshold).

Next, process 300 proceeds to act 310, where a statistical model forpredicting the onset of a motor task is trained using the training datagenerated at act 308.

The statistical model being trained may take as input a sequence of datasets each of which comprises an n-dimensional vector of EMG sensor data.The statistical model may provide output that indicates, for each of oneor more motor tasks that may be performed by a user, the likelihood orprobability that the onset of a motor task will occur within a thresholdamount of time in the future. For example, the statistical model maytake as input a sequence of vectors {x_(k)|1≦k≦K} generated usingmeasurements obtained at time points t₁, t₂, . . . , t_(K), where theith component of vector x_(j) is a value measured by the ith EMG sensorat time t_(j) and/or derived from the value measured by the ith EMGsensor at time t_(j). Based on such input, the statistical model mayprovide output indicating, for each action in a set of actions (whichset may include a member corresponding to not taking any action), aprobability that the onset of the action will occur within a thresholdamount of time in the future, which threshold may be the thresholdselected at act 306. As one non-limiting example, the statistical modelmay be trained to anticipate which one of multiple keys on a keypad theuser will press within the threshold amount of time in the future. Inthis example, the statistical model may output, for each key on thekeypad, a value indicating the probability that the user will press thatkey within the threshold amount of time in the future. Additionally, thestatistical model may provide output indicating the probability that theuser will not press any key within the threshold amount of time in thefuture.

In some embodiments, the statistical model may be a neural network and,for example, may be a recurrent neural network. In some embodiments, therecurrent neural network may be a long short-term memory (LSTM) neuralnetwork. It should be appreciated, however, that the recurrent neuralnetwork is not limited to being an LSTM neural network and may have anyother suitable architecture. For example, in some embodiments, therecurrent neural network may be a fully recurrent neural network, arecursive neural network, a Hopfield neural network, an associativememory neural network, an Elman neural network, a Jordan neural network,an echo state neural network, a second order recurrent neural network,and/or any other suitable type of recurrent neural network. In otherembodiments, neural networks that are not recurrent neural networks maybe used. For example, deep neural networks, convolutional neuralnetworks, and/or feedforward neural networks, may be used.

In some of the embodiments in which the statistical model is a neuralnetwork, the neural network may include a softmax layer such that theoutputs add up to one and may be interpreted as probabilities. Theoutput of the softmax layer may be a set of values corresponding to arespective set of actions, with each value indicating a probability thatthe user will perform a respective action in the set of actions within athreshold amount of time in the future. As one non-limiting example, theoutput of the softmax layer may be a set of three probabilities (e.g.,0.92, 0.05, and 0.03) indicating the respective probabilities that theuser will press one of the keys “A”, “B”, and “C” on a keypad within athreshold amount of time in the future (e.g., within 250 milliseconds inthe future and/or any other suitable threshold, examples of which areprovided herein).

It should be appreciated that when the statistical model is a neuralnetwork, the neural network is not required to produce outputs that addup to one. For example, instead of a softmax layer, the output layer ofthe neural network may be a sigmoid layer (which has no restriction thatthe probabilities add up to one). In such embodiments, the neuralnetwork may be trained with a sigmoid cross-entropy cost. Such animplementation may be advantageous in the case when multiple differentactions may occur within a threshold amount of time and it is notimportant to distinguish the order in which these actions occur (e.g., auser may press two buttons at the same time with the threshold amount oftime). In some embodiments, any other suitable non-probabilisticmulti-class classifier may be used, as aspects of the technologydescribed herein are not limited in this respect.

It should be appreciated that aspects of the technology described hereinare not limited to using neural networks, as other types of statisticalmodels may be employed in some embodiments. For example, in someembodiments, the statistical model may comprise a hidden Markov model(HMM), a switching HMM with the switching allowing for toggling amongdifferent dynamic systems, dynamic Bayesian networks, and/or any othersuitable graphical model having a temporal component. Any suchstatistical model may be trained at act 310 using the EMG data obtainedat act 302.

As another example, in some embodiments, the statistical model may be aclassifier taking as input, features derived from the EMG data obtainedat act 302. In such embodiments, the classifier may be trained at act310 using features extracted from the EMG data obtained at act 302. Theclassifier may be a support vector machine, a Gaussian mixture model, aregression based classifier, a decision tree classifier, a Bayesianclassifier, and/or any other suitable classifier, as aspects of thetechnology described herein are not limited in this respect. Inputfeatures to be provided to the classifier may be derived from the EMGdata obtained at act 302 in any suitable way. For example, the EMG datamay be analyzed as time series data using wavelet analysis techniques(e.g., continuous wavelet transform, discrete-time wavelet transform,etc.), Fourier-analytic techniques (e.g., short-time Fourier transform,Fourier transform, etc.), and/or any other suitable type oftime-frequency analysis technique. As one non-limiting example, the EMGdata may be transformed using a wavelet transform and the resultingwavelet coefficients may be provided as inputs to the classifier.

In some embodiments, at act 310, values for parameters of thestatistical model may be estimated from the training data generated atact 308. For example, when the statistical model is a neural network,parameters of the neural network (e.g., weights) may be estimated fromthe training data. In some embodiments, parameters of the statisticalmodel may be estimated using gradient descent, stochastic gradientdescent, and/or any other suitable iterative optimization technique. Inembodiments where the statistical model is a recurrent neural network(e.g., an LSTM), the statistical model may be trained using stochasticgradient descent and backpropagation through time. The training mayemploy a cross-entropy loss function and/or any other suitable lossfunction, as aspects of the technology described herein are not limitedin this respect.

Next, process 300 proceeds to act 312, where an operating threshold isdetermined for the statistical model generated at act 310. The operatingthreshold may be used for interpreting the output of the statisticalmodel when predicting whether the onset of a particular action willoccur in a threshold amount of time in the future. For example, when thestatistical model provides a set of values, each of which indicates theprobability that the onset of a respective action will occur within athreshold amount of time (e.g., as set by the prediction timescalethreshold), the probabilities may be compared to the operating thresholdand a prediction may be made based on the comparison. For example, itmay be predicted that the user will perform a particular action withinthe threshold amount of time when the probability for that particularaction exceeds the operating threshold.

As an example, suppose that the output of the statistical model is a setof three probability values (e.g., 0.8, 0.15, and 0.05) indicating therespective probabilities that the user will press one of the keys “A”,“B”, and “C” on a keypad within a threshold amount of time in the futureand that the operating threshold is set at 0.75. Because 0.8 is greaterthan the operating threshold of 0.75, a prediction may be made that theuser will press the key “A” within the threshold amount of time. On theother hand, if the operating threshold was set to be greater than 0.8(e.g., 0.9), a prediction may be made that the user will not press anyof the keys “A,” “B,” and “C” because the probability values in theoutput vector of the statistical model are all less than the operatingthreshold.

In some embodiments, the operating threshold may be determined, at act312, by characterizing performance of the trained statistical modelusing at least some of the EMG data obtained at act 302 for differentcandidate operating thresholds. In some embodiments, at least some ofthe EMG data obtained at act 302 may be held out and not used to trainthe statistical model at act 310. The held out data may be used fordetermining the operating threshold at act 312 of process 300 at leastin part by characterizing performance of the trained statistical modelfor different candidate operating thresholds.

The inventors have appreciated that certain performance characteristicsof the statistical model (e.g., false positive rate and the meananticipation time) may depend on the value of the operating thresholdused. The mean anticipation time may indicate how far in advance of whenthe predicted action is to take place, on average, the statistical modelmay be able to correctly predict that the user will perform thepredicted action. In some embodiments, the held out EMG data may be usedto calculate at least one performance characteristic (e.g., falsepositive rate, false negative rate, true positive rate, true negativerate, positive predictive value, negative predictive value, meananticipation time, any other performance characteristic that can bederived from these quantities, etc.) for each of the different candidateoperating thresholds. An operating threshold may then be selected basedon the analysis of the held out data and target performancecharacteristics. For example, a false positive rate and a meananticipation time may be obtained, using held out EMG data, for each oneof multiple different operating thresholds. Based on a target falsepositive rate (e.g., 15%) and a target mean anticipation time (e.g., 150milliseconds), the candidate operating threshold associated with a falsepositive rate closest to the false positive rate and a mean anticipationtime closest to the target mean anticipation time may be selected. Insome embodiments, a user may specify (e.g., via a user interface) atleast one performance characteristic for a task to be performed and theoperating threshold may be determined based on the specified at leastone performance characteristic.

It should also be appreciated that, in some embodiments, when thestatistical model is trained to predict the onset of any one of multipledifferent actions, different operating thresholds may be used fordifferent actions. For example, when a statistical model is trained topredict whether a user will press one of two different buttons,different operating thresholds may be used for the different buttons.Using different operating thresholds is especially valuable inapplications where there are different (e.g., greater or lesser)benefits for anticipating different actions and different (e.g., greateror lesser) costs for false positives.

Next, process 300 proceeds to act 314, where the trained statisticalmodel and/or the operating threshold are stored (e.g., in datastore214). The trained statistical model and/or operating threshold may bestored using any suitable format, as aspects of the technology describedherein are not limited in this respect. In this way, the statisticalmodel and operating threshold generated during execution of process 300may be used at a later time, for example, in accordance with the processdescribed with reference to FIG. 4.

It should be appreciated that process 300 is illustrative and thatvariations of the process are contemplated. For example, although in theillustrated embodiment, the statistical model is trained to predictwhether a motor action will occur within a threshold amount of time, inother embodiments, the statistical model may be trained to output adistribution over time, indicating probabilities that the motor actionwill occur at various time points in the future. Such a distribution maybe discrete (e.g., specified via a point mass function) or a continuousdistribution (e.g., specified via a probability density function). Sucha distribution may be used to determine whether a motor action willoccur with a threshold amount of time by summing or integrating thedistribution as appropriate (e.g., integrating the distribution todetermine the amount of probability mass to the right of the threshold).

As another example, although in the illustrated embodiment, thestatistical model is trained to output a probability that an actionoccurs within a threshold amount of time, in other embodiments, thestatistical model may be configured to output any suitable type ofoutput indicating a prediction of whether the action will occur withinthe threshold amount of time. Such a prediction need not be aprobability. For example, such a prediction may be binary (e.g., yes orno) indicating whether the output action will or will not occur withinthe threshold amount of time. As another example, such a prediction maybe numeric, but not a probability in that the prediction may be a valuegreater than one (e.g., an un-normalized likelihood). In embodimentswhere the prediction output by the statistical model is not aprobability value, the operating threshold may be replaced by anothersuitable hyper-parameter allowing for controlling the tradeoff between aperformance measure and mean anticipation rate or may be omittedaltogether.

FIG. 4 illustrates a process 400 for predicting a motor action performedby a user based on recorded EMG signals and a trained statistical modelin accordance with some embodiments. Although process 400 is describedherein with respect to EMG signals, it should be appreciated thatprocess 400 may be used to predict a motor action performed by a userbased on any recorded neuromuscular signals including, but not limitedto, EMG signals, MMG signals, SMG signals, or any suitable combinationthereof and a trained statistical model trained on such neuromuscularsignals.

Process 400 begins in act 410, where EMG signals are recorded from aplurality of EMG sensors arranged on or near the surface of a user'sbody to record neuromuscular activity as the user performs a motoraction. In one example described above, the plurality of EMG sensors arearranged circumferentially (or otherwise oriented) on wearable deviceconfigured to be worn on or around a part of the user's body. In someembodiments, the plurality of EMG signals is recorded continuously as auser wears the wearable device including the plurality of EMG sensors.Process 400 then proceeds to act 412, where the raw EMG signals recordedby electrodes in the EMG sensors are optionally processed. For example,the EMG signals may be processed using amplification, filtering, orother types of signal processing (e.g., rectification). In someembodiments, filtering includes temporal filtering implemented usingconvolution operations and/or equivalent operations in the frequencydomain (e.g., after the application of a discrete Fourier transform).

Process 400 then proceeds to act 414, where the EMG signals are providedas input to a statistical model (e.g., a neural network) trained usingone or more of the techniques described above in connection with process300. In some embodiments that continuously record EMG signals, thecontinuously recorded EMG signals (raw or processed) may be continuouslyor periodically provided as input to the trained statistical model forprediction of the onset of one or more motor actions performed by theuser. As discussed above, in some embodiments, the trained statisticalmodel is a user-independent model trained based on EMG measurements froma plurality of users. In other embodiments, the trained model is auser-dependent model trained on EMG data recorded from the individualuser data from which the EMG data is recorded in act 410.

After the trained statistical model receives the EMG data as a set ofinput parameters, process 400 proceeds to act 416, where the probabilityof one or more motor actions occurring within a particular timethreshold (e.g., the prediction timescale threshold discussed above) isoutput from the trained statistical model. Also, as discussed above, insome embodiments, the output of the trained statistical model may be aset of probability values (e.g., 0.8, 0.15, and 0.05) indicating therespective probabilities that the user will perform a respective actionwithin a threshold amount of time in the future. The prediction ofwhether and/or what motor action the user will perform within thethreshold amount of time may be determined by comparing the output setof probability values with an operating threshold set for a particulartask or application. Non-limiting examples of setting an operatingthreshold for predicting one or more motor actions are discussed above.In embodiments, where the output of the statistical model is anon-probabilistic prediction, another suitable hyper-parameter may beused instead of the operating threshold to optimize across tradeoffs inperformance characteristics, as discussed above.

After a motor action is predicted in act 416, process 400 proceeds toact 418, where a control signal is transmitted to a device based, atleast in part, on the predicted motor action. Preferably the controlsignal is transmitted to the device as soon as possible following theprediction in act 416 to increase the amount of time between when thecontrol signal based on the prediction is sent to the device and thetime when the control signal would have been sent had the control signalbeen sent in response to completion of the motor action.

As discussed briefly above, in some embodiments at least some of thecontinuously recorded EMG data may be used to retrain the statisticalmodel (or train the statistical model de novo) to enable the model tolearn the statistical relationships between neuromuscular activityrecorded by the EMG sensors and motor actions performed by a particularuser. Continuous training of the statistical model may result inimproved performance of the model in predicting actions that areperformed by the user in a consistent manner.

Although process 400 is described herein as being performed afterprocess 300 has completed and a statistical model has been trained, insome embodiments, process 300 and 400 may be performed together. Forexample, the statistical model may be trained in real-time, as a user isinteracting with a control interface of a device and the trainedstatistical model may be used as soon as the model has been trainedsufficiently to provide reliable predictions.

In the examples discussed above, aspects of the technology are describedin the context of predicting the onset of single motor task (e.g., theprobability of whether the user will press a button or not) orpredicting the onset of a motor task from among a plurality of motortasks (e.g., the probability of whether the user will press a particularbutton from among three buttons). In other embodiments, a statisticalmodel trained in accordance with one or more of the techniques describedherein may be used to predict the probability that the user will performa sequence of motor actions. For example, rather than only predictingwhether a user pressed button “A,” “B,” or “C,” some embodiments aredirected to predicting whether the user pressed the sequence of buttons“ABC,” “ABA” or “CAB.” Although the process of selecting data to trainthe statistical model may differ from the examples described above, theprocess of training the model, selecting a prediction timescalethreshold and an operating threshold proceed similarly in embodiment inwhich a sequence of motor actions is predicted.

The above-described embodiments can be implemented in any of numerousways. For example, the embodiments may be implemented using hardware,software or a combination thereof. When implemented in software, thesoftware code can be executed on any suitable processor or collection ofprocessors, whether provided in a single computer or distributed amongmultiple computers. It should be appreciated that any component orcollection of components that perform the functions described above canbe generically considered as one or more controllers that control theabove-discussed functions. The one or more controllers can beimplemented in numerous ways, such as with dedicated hardware or withone or more processors programmed using microcode or software to performthe functions recited above.

In this respect, it should be appreciated that one implementation of theembodiments of the present invention comprises at least onenon-transitory computer-readable storage medium (e.g., a computermemory, a portable memory, a compact disk, etc.) encoded with a computerprogram (i.e., a plurality of instructions), which, when executed on aprocessor, performs the above-discussed functions of the embodiments ofthe present invention. The computer-readable storage medium can betransportable such that the program stored thereon can be loaded ontoany computer resource to implement the aspects of the present inventiondiscussed herein. In addition, it should be appreciated that thereference to a computer program which, when executed, performs theabove-discussed functions, is not limited to an application programrunning on a host computer. Rather, the term computer program is usedherein in a generic sense to reference any type of computer code (e.g.,software or microcode) that can be employed to program a processor toimplement the above-discussed aspects of the present invention.

Various aspects of the present invention may be used alone, incombination, or in a variety of arrangements not specifically discussedin the embodiments described in the foregoing and are therefore notlimited in their application to the details and arrangement ofcomponents set forth in the foregoing description or illustrated in thedrawings. For example, aspects described in one embodiment may becombined in any manner with aspects described in other embodiments.

Also, embodiments of the invention may be implemented as one or moremethods, of which an example has been provided. The acts performed aspart of the method(s) may be ordered in any suitable way. Accordingly,embodiments may be constructed in which acts are performed in an orderdifferent than illustrated, which may include performing some actssimultaneously, even though shown as sequential acts in illustrativeembodiments.

Use of ordinal terms such as “first,” “second,” “third,” etc., in theclaims to modify a claim element does not by itself connote anypriority, precedence, or order of one claim element over another or thetemporal order in which acts of a method are performed.

Such terms are used merely as labels to distinguish one claim elementhaving a certain name from another element having a same name (but foruse of the ordinal term).

The phraseology and terminology used herein is for the purpose ofdescription and should not be regarded as limiting. The use of“including,” “comprising,” “having,” “containing”, “involving”, andvariations thereof, is meant to encompass the items listed thereafterand additional items.

Having described several embodiments of the invention in detail, variousmodifications and improvements will readily occur to those skilled inthe art. Such modifications and improvements are intended to be withinthe spirit and scope of the invention. Accordingly, the foregoingdescription is by way of example only, and is not intended as limiting.The invention is limited only as defined by the following claims and theequivalents thereto.

What is claimed is:
 1. A control system, comprising: a plurality ofsensors configured to continuously record a plurality of neuromuscularsignals from a user; and at least one computer processor programmed to:provide as input to a trained statistical model, the plurality ofneuromuscular signals and/or information based on the plurality ofneuromuscular signals; predict, based on an output of the trainedstatistical model, whether an onset of a motor action will occur withina threshold amount of time; and send a control signal to at least onedevice based, at least in part, on the output probability, wherein thecontrol signal is sent to the at least one device prior to completion ofthe motor action by the user.
 2. The control system of claim 1, whereinthe output of the trained statistical model comprises a probability thatthe onset of the motor action will occur within the threshold amount oftime, and wherein predicting that an onset of a motor action will occurwithin a threshold amount of time comprises determining whether theprobability is greater than an operating threshold.
 3. The controlsystem of claim 2, wherein the at least one computer processor isfurther programmed to determine the operating threshold based, at leastin part, on an analysis of data obtained from neuromuscular signalsrecorded by at least some of the plurality of sensors.
 4. The controlsystem of claim 3, wherein determining the operating thresholdcomprises: determining a mean anticipation time based on the data; anddetermining the operating threshold based, at least in part, on the meananticipation time.
 5. The control system of claim 2, wherein the atleast one computer processor is further programmed to receive at leastone performance characteristic for a task to be performed, and whereinthe operating threshold is determined based, at least in part, on thereceived at least one performance characteristic.
 6. The control systemof claim 5, wherein the at least one performance characteristic includesa performance characteristic selected from the group consisting of afalse positive rate, a false negative rate, a true positive rate, a truenegative rate, a positive predictive value, a negative predictive value,and a mean anticipation time.
 7. The control system of claim 1, whereinthe output of the trained statistical model comprises a set ofprobabilities, each probability in the set indicating a probability thatthe onset of a respective motor action of a plurality of motor actionswill occur within the threshold amount of time, and wherein predictingthat an onset of a motor action will occur within a threshold amount oftime comprises determining whether any of the probabilities in the setof probabilities is greater than an operating threshold stored by thesystem.
 8. The control system of claim 1, wherein the output of thetrained statistical model comprises a probability that the onset of asequence of motor actions will occur within the threshold amount oftime, and wherein predicting that an onset of a motor action will occurwithin a threshold amount of time comprises determining whether theprobability is greater than an operating threshold.
 9. The controlsystem of claim 1, wherein the at least one computer processor isfurther programmed to retrain the trained statistical model based, atleast in part, on at least some of the plurality of neuromuscularsignals.
 10. The control system of claim 1, wherein the trainedstatistical model comprises a recurrent neural network.
 11. The controlsystem of claim 1, wherein the at least one computer processor isfurther programmed to: determine a set of features based on theplurality of neuromuscular signals; and provide as input to a trainedstatistical model, the set of features as information based on theplurality of neuromuscular signals, wherein predicting, based on anoutput of the trained statistical model, that an onset of a motor actionwill occur within a threshold amount of time comprises classifying theset of features using the trained statistical model.
 12. The controlsystem of claim 1, wherein the plurality of sensors are arranged on awearable device configured to be worn on or around a body part of theuser.
 13. The control system of claim 12, wherein the wearable devicecomprises a flexible or elastic band configured to be worn around thebody part of the user.
 14. The control system of claim 13, wherein thewearable device comprises an armband configured to be worn around an armof the user.
 15. The control system of claim 1, further comprisinghardware circuitry and/or software configured to process theneuromuscular signals prior to being provided as input to the trainedstatistical model, wherein processing the neuromuscular signalscomprises processing selected from the group consisting ofamplification, filtering, and rectification.
 16. The control system ofclaim 1, wherein sending the control signal to the at least one deviceprior to completion of the motor action by the user comprises sendingthe control signal at least 25 ms prior to the completion of the motoraction by the user.
 17. The control system of claim 1, wherein the atleast one device comprises at least one device selected from the groupconsisting of a video game system, a consumer electronics device, amanned vehicle, an unmanned vehicle, a robot, and a weapon.
 18. Thecontrol system of claim 1, wherein the plurality of sensors comprisessensors selected from the group consisting of electromyography (EMG)sensors, mechanomyography (MMG) sensors, and sonomyography (SMG)sensors.
 19. A method of predicting an onset of a motor action, themethod comprising providing, as input to a trained statistical model, aplurality of neuromuscular signals recorded from a plurality of sensorsarranged on or around a part of a user's body; predicting, using atleast one computer processor, whether an onset of a motor action willoccur within a threshold amount of time; and sending a control signal toat least one device based, at least in part, on the output probability,wherein the control signal is sent to the at least one device prior tocompletion of the motor action by the user.
 20. A computer system fortraining a statistical model to predict an onset of a motor task based,at least in part, on neuromuscular signal data, the computer systemcomprising: an input interface configured to: receive the neuromuscularsignal data recorded during performance of a motor action performed byone or more users; receive result data indicating an outcome of themotor action performed by the one or more users; and at least onestorage device configured to store a plurality of instructions that,when executed by at least one computer processor perform a method of:generating training data based, at least on part, on the receivedneuromuscular signal data and the received result data; training thestatistical model using at least some of the generated training data tooutput a trained statistical model; determining an operating thresholdused to interpret output of the trained statistical model; and storing,by the at least one storage device, the trained statistical model andthe operating threshold, wherein the trained statistical model isconfigured to predict the onset of a motor task prior to completion ofthe motor task by a user.